Temporal event data is common in many fields. Temporal event data typically comprises a set of entities undergoing various events, such as patients that are diagnosed with various symptoms or call center tickets that move from an initial call state to a resolution state. Electronic medical records (EMRs), for example, are proliferating throughout the healthcare system and contain a large amount of temporal disease events such as diagnosis dates and the onset dates for various symptoms. At major medical institutions, such as hospitals, insurance companies and large medical groups, these databases contain large amounts of historical patient data complete with patient profile information, structured observational data (such as diagnosis codes and medications), as well as unstructured physician notes.
The information in these databases can be useful in guiding the diagnosis of incoming patients or in clinical studies of a disease. The vast amount of information, however, can be overwhelming and makes these datasets difficult to analyze. Analyzing disease progression pathways in terms of these observed events, for example, can provide important insights into how diseases evolve over time. Moreover, correlating these pathways (i.e., flows) with the eventual outcomes of the corresponding patients can help clinicians understand how certain progression paths may lead to better or worse outcomes. For example, medical professionals are often interested in understanding how various symptoms of a given disease, and their order of onset, correlate with patient outcome.
A need therefore exists for methods and apparatus for temporal graph-based visualization of event data. A further need exists for methods and apparatus for aggregating the temporal event data to identify common states and transitions between states. In addition, a need exists for methods and apparatus for identifying correlations between these pathways with eventual outcomes.